Sampling-based motion planners are well established tools that can rapidly find solutions to complex problems [1]}, [2]}, [3]}, [4]}, [5]}. The first developed planners deal with deterministic transitions and operate solely in the state space [2]}, [7]}. Later formulations extend these deterministic planners to obtain asymptotically-optimal sampling-based planners such as SST and SST* [5]}, and incorporate motion uncertainty via chance constraints on the probability of collision (CC-RRT), [3]}, [4]}, [11]}. These techniques have more recently been extended to accommodate measurement uncertainty with Gaussian belief trees [12]}, [13]}, and feedback-based information roadmaps [14]}. However, while these fast and efficient methods have been extended to accommodate other uncertainties,
no sampling-based motion planning algorithm currently exists that incorporate ET estimation.
